The use of curvature and bias measures to discriminate among equilibrium moisture equations for mustard seed

Barrozo, M. A. S.; Silva, A.A.M.; Oliveira, Daniel Tostes

doi:10.1016/j.jspr.2007.05.002

Cited by 12 publications

(9 citation statements)

References 22 publications

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“…The applicability of Henderson model is still confirmed in different reports (see for example: (Barrozo, Silva, & Oliveira, 2008;Cervenka, Rezkova, & Kralovsky, 2008;García-Pérez et al, 2008;Peng, Chen, Wu, & Jiang, 2007;Sinija & Mishra, 2008)). Pfost (1967a, 1967b) developed their model of water sorption on foodstuffs considering the changes in the value of the free energy during sorption with the moisture content.…”

Section: Henderson Modelmentioning

confidence: 71%

See 1 more Smart Citation

Searching the most optimal model of water sorption on foodstuffs in the whole range of relative humidity

Furmaniak¹,

Terzyk²,

Gołembiewski³

et al. 2009

Food Research International

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Section: Henderson Modelmentioning

confidence: 71%

“…Despite this the Chung and Pfost model has found applicability for description of many experimental data (Barrozo et al, 2008;Basu et al, 2006;Iguaz & Vírseda, 2007;Samapundo et al, 2007;Tirawanichakul et al, 2008).…”

Section: Chung and Pfost Modelmentioning

confidence: 99%

Searching the most optimal model of water sorption on foodstuffs in the whole range of relative humidity

Furmaniak¹,

Terzyk²,

Gołembiewski³

et al. 2009

Food Research International

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“…When the LS estimators present small bias, near-normal distribution and almost constant variances, it can be stated that the estimators present a near-linear behavior and, consequently, the inferences will be more reliable [21]. However, for nonlinear regressions, these properties are valid only when the sample size is large enough.…”

Section: Nonlinearity Measuresmentioning

confidence: 99%

“…Consequently, it can be stated that the results become more applicable as the sample size increases. When the LS estimators present small bias, near-normal distribution and almost constant variances, it can be stated that the estimators present a near-linear behavior and, consequently, the inferences will be more reliable [21]. The extent of the bias, the deviation from normal distribution and the excess variance differ greatly from model to model.…”

Section: Nonlinearity Measuresmentioning

confidence: 99%

Drying Kinetics of Silica Gel: Statistical Discrimination Using Nonlinearity Measures

et al. 2012

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The objective of the present work was to analyze the drying kinetics of silica gel based on an experimental study performed in a thin-layer dryer and a statistical discrimination of the main drying kinetic equations. Most semi-empirical drying kinetics equations presented in the literature are nonlinear; thus, care should be taken when estimating parameters, since in some situations the estimators may not be appropriate. There are procedures available to validate the statistical properties of the least squares estimators of nonlinear models. In this study, five semi-empirical drying kinetics equations were discriminated using measures of curvature and bias. The results showed that the Overhults equation is the best one to describe the drying kinetics of silica gel.

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“…Box [10] presented a useful formula for estimating the bias in the LS estimators; Bates and Watts [11] developed new measures of nonlinearity based on the geometric concept of curvature. Such procedures have been used in other studies to choose the best model [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

The Use of Nonlinearity Measures in the Estimation of Kinetic Parameters of Sugarcane Bagasse Pyrolysis

et al. 2010

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The majority of the pyrolysis kinetics models presented in the literature is nonlinear. In these models, the influence of temperature is normally described by the Arrhenius equation. In some studies related to parameter estimation of nonlinear models suggestions can be found for reparameterization of the Arrhenius-type equation due to inadequate results on the parameter estimation. In this work, kinetic modeling is proposed in order to predict the primary biomass decomposition behavior under a dynamic regime. This model is based on the traditional model of parallel reactions and uses the Arrhenius-type equation in the reparameterized form. Some nonlinearity measures are used as a tool to correctly estimate the kinetic parameters of the pyrolysis of sugarcane bagasse. The predictions from the proposed model properly reproduced the experimental DTG curves. The reparametrized model showed non-significance for bias and nonlinearity measures.

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The use of curvature and bias measures to discriminate among equilibrium moisture equations for mustard seed

Cited by 12 publications

References 22 publications

Searching the most optimal model of water sorption on foodstuffs in the whole range of relative humidity

Searching the most optimal model of water sorption on foodstuffs in the whole range of relative humidity

Drying Kinetics of Silica Gel: Statistical Discrimination Using Nonlinearity Measures

The Use of Nonlinearity Measures in the Estimation of Kinetic Parameters of Sugarcane Bagasse Pyrolysis

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